Optimizing Image Feature Extraction and Selection: A Comprehensive
Review with Spark Case Studies
Abstract
As benchmark image datasets expand in sample size and feature
complexity, the challenge of managing increased dimensionality becomes
apparent. Contrary to the expectation that more features equate to
enhanced information and improved outcomes, the curse of dimensionality
often hampers performance. This paper reviews existing literature on
filter feature selection techniques applied to image features,
highlighting when they are applied to both classical and
deep-learning-based feature extraction methods. Additionally, this study
explores how different feature selection methods behave when applied to
image features through big data technologies. Different experiments were
performed to compare the results when using feature selection techniques
with various reduction percentages. Experimental results demonstrated
that an important reduction of the extracted features provides
classification results similar to those obtained with the full set of
features. Furthermore, applying dimensionality reduction techniques
outperforms, in some cases, the results achieved using broad feature
vectors.
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